When new or existing restaurant owners are trying to decide on a new location for their restaurant, there are multiple key factors for them to consider and understand before they decide on a new location.
Based on my initial research of restaurant location services and restaurant owner magazines, I identified several key factors necessary for choosing a restaurant business location.
The key factors for site location include visibility, parking, space size, crime rates, surrounding businesses and competitor analysis, accessibility, and safety. This criteria likely applies to other small to medium sized businesses.
For the purpose of my Capstone project, I have narrowed my choice to the area of “Understanding Surrounding Businesses and Competitor analysis for New Restaurant Locations” as the key problem for my project to address.
I begin with Lisa Melbourne, a fictitous and hypothetical person who is the restaurant owner of “Breaking Bread”, a highly successful breakfast-only restaurant operating in Southern California. Lisa is looking to expand her restaurant operations into Northern California, with key interest to find a location within the San Francisco South Bay region. Lisa has enlisted my services and needs my research and recommendations to identify a city and location with increasing/high cross-business traffic and area having fewest breakfast venues within a couple of miles so she is not launching her restaurant into a competitive atmosphere.
The following data science approach phases will be performed on this project:
My goal is to provide Lisa with the research data she requires so she can effect an informative decision regarding a new restaurant location. The collection process must include all of the following:
First dataset is neighborhood data comprised using postal codes found for San Jose and immediately surrounding areas. San Jose postal zipcode data was scraped from the following data sources: https://tools.usps.com/find-location.htm https://www.postallocations.com/ca/county/santa-clara The zipcodes will be useful for defining neighborhood references and for geo mapping.
Project will comprise data from a mixture of csv files, GET Requests in json form, Venue API json data, and geo location api.
https://www.sanjose.org/restaurants?field_city_value=san jose#restaurants-listing
Foursquare data through API will be used to meet several requirements:
Population Density dataset and analysis in our region of restaurant location interest is retrieve from california population dataset arranged by zipcode. Data is available from https://www.california-demographics.com/zip_codes_by_population".
The goal and thought for population data is for data inference so we can deduce properties of underlying distribution. Sales, revenue, and venue traffic are all driven by supply and demand. By referencing California population data on zip codes, the goal here is understand restaurant ratio to dense and low populated areas. Population density can help customer target not only restaurant location but unmet area needs on food selection.
Bottom line, it's for understanding potential venue growth traffic, to a small area, as well as geographical opportunities for property placement based on this notion.
For Competitive Analysis
Foursquare data formats we will use include: v2/venues/search, v2/venues/explore, v2/venues/nextvenues
Everything completed as required and in some cases exceeded original plan.
One drawback was getting reliable postal information.
Regarding the IBM Data Science Professional Program. First off, thank you to the IBM teaching staff, technical team, assistants, and learning facilitators between IBM and Coursera. This has been a professional, fun, and good challenging experience. I also appreciate my time with my fellow students. This has been a good journey.
A few parting thoughts upon IBM Data Science Professional course conclusion:
I personally recommend the IBM Data Science Professional for project and program managers, architects, engineers, medical, finance, biotech, medicine or anyone with a business need or hobby, who wants a solution for telling stories with data and visualizations.
Course modules are structured and enriched with coursework and information you will save and use as future reference. I am confident with the top experts developing curriculum and conducting the training with Coursera, fasciliting, training and delivering highest quality learning experiences. The Data Science Professional courses I took through Coursera were all IBM sponsored with solid instructors and PhDs who are well experienced and who provide full and exciting assignment challenges.
I highly encourage others to take a course or two online to get a taste.
With python and applied data science and machine learning, we can identify data solutions for a vast array of client needs and in many cases be more effective than conventional means, i.e, moving away from spreadsheets and moving onto Pandas. Finance folks are seeing this learning opportunity at a growing pace.
We can explore and identify many customer use and problem cases with stronger precision and accuracy than conventional business methods. The toolbox is large. This is not a broad statement, as numerous leading edge tools still have their place.
This particular project required research from a variety of locations where data was stored and retrieved with quick need for working form with data harnessed in a variety of formats. It was pleasant leap into advanced technicals and thinking outside the box in crunch time.
Thank you.
I can be reached at ericluiggi@gmail.com. Feel free to reach out to me, provide your review or drop a comment with. Thanks again!